code: def __init__(
      self,
      agent_parameters,
      parent: Union["LevelManager", "CompositeAgent"] = None,
  ):
      super().__init__(agent_parameters, parent)
      self.last_gradient_update_step_idx = 0
      self.q_values = self.register_signal("Q Values")
      self.value_loss = self.register_signal("Value Loss")
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters
  from rl_coach.core_types import EnvironmentSteps
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
  from rl_coach.utils import last_sample
label_desc: Use image node_id 1 to create a new NStepQAgent object from inherited base classes: ValueOptimizationAgent, PolicyOptimizationAgent with example: obj = NStepQAgent(agent_parameters, parent)